Point cloud registration is one of the key technologies for indoor scene reconstruction based on the RGB-D(RGB-depth) sensor. To solve the point cloud registration problem among key frames in sparse mapping, this study proposes a coarse registration algorithm with scene classification based on improved random sample consensus (RANSAC). First, geometric information and photometric information are used to detect, describe, and match keypoints. Then, the scene classification algorithm determines the scene category, and geometric and photometric correspondences are adaptively combined. Finally, the improved RANSAC is proposed to estimate the transformation among key frames by biased random sampling and adaptive hypothesis evaluation. The whole coarse registration algorithm is experimentally verified by the public RGB-D dataset and compared with several algorithms. Experimental results show that the coarse registration algorithm can achieve robust and effective transformation estimation, which is helpful for subsequent fine registration and overall indoor scene reconstruction.